Abstract In this paper we introduce a new analytical approach for management of waterfloods in heterogeneous reservoirs. The main contribution is the development of a process and metric to evaluate the pair-wise injector-producer (IP) relationships, i.e., to quantify the impact of any injection well on the neighboring producing wells. The proposed metric is particularly designed to consider the non-linearity of the IP relationship between the injection and production rates by using the Mutual Information (MI) data mining tool. Non-linearity of the IP relationship is the main challenge in quantifying this relationship and, to the best of our knowledge, this is the first time that MI is used in the petroleum literature for IP relationship identification. In addition to MI that captures the non-linear correlation in the IP relationship, our metric considers other parameters such as the distance between the IP pair as well as their relative injection and production rates, respectively. Leveraging our proposed metric, we propose a system, for optimal waterflooding with which a field engineer can automatically:Identify the under-performing producers based on their performance characteristics such as wateroil ratio, gas oil ratio, and oil production rate;
Rank all injectors based on their impact on the under-performing producers using our proposed IP relationship identification metric;
Decide on optimal injection volumes for individual injectors that have the most impact on the under-performing producers and maximize the recovery factor.
The proposed technique can significantly reduce the decision-making time for the effective management of complex waterflood.
1. Introduction In petroleum reservoirs, enabling engineers to model - and hence predict - injector-producer relationships is a key to gain maximum oil production with minimum operation costs. From studying the historical injection and production data from a reservoir, one can see that the production performance is controlled by various factors. One of the important factor, of course is the injection volumes. Examples of other factors include (but are not limited to) distance between each injector-producer pair, heterogeneities in subsurface structures, anisotropy in permeability, and rock property changes by drop in reservoir temperature. Typically, certain parameters may affect the output to a larger extent as compared with others, whereas some may have no effect on the behavior of the system at all. This complex behavior of oilfields renders the identification of the allocation factors between injectors and producers as an extremely hard problem [1].
Some existing methods provide allocation factors between injectors and producers based on the past experience, production and injection historical data and numerical simulations. These approaches are not accurate because they ignore the non-linear relationships between various parameters. In this paper, we present a novel mutual information based metric to model the non-linear relationships between injectors and producers in oilfields. Intuitively, mutual information measures the information that two separate variables share; i.e., it measures how much knowing one of these variables reduces our uncertainty about the other. In this case, we use mutual information to determine how much knowing about an injector can reduce our uncertainty about a producer. In other words, mutual information can help us quantify the dependence between injectors and producers in their non-linear relationship.